3 Useful Analytics Trends You Should Adopt
Business analytics continues to be a sweet spot for enterprises looking to go digital. However, the market is riddled with a number of analytics solutions, many of which are not market-ready to the deliver the value they ought to. CIOs must be able to differentiate these nascent offerings from those that have reached maturity levels and can actually give the required digital boost to the organization. This is critical to ensure success in analytics-related fields such as artificial intelligence (AI), embedded analytics and natural language processing (NLP).
We analyze three top big data and analytics trends and assess them from a maturity standpoint.
1. Artificial Intelligence (AI): Machine learning and deep learning together are being touted as the next big thing related to analytics. Those who started exalting analytics are now turning to AI, many of whom are stating that AI will soon replace all kinds of manual tasks and make humans redundant. However, this is a far cry from the current maturity levels of AI, which is still at the stage of figuring out real-world use cases. Organizations must stop thinking full-fledged automation and rather, turn to assistive intelligence, a field where analyst or business user skills are improved by applying embedded advanced analytic capabilities. Many enterprises are already utilizing this to prepare and organize large amounts of data and analysis means such as pattern detection, anomaly detection and so on. Assistive intelligence is a more real-world application for AI as of today.
2. Natural Language Processing and Natural Language Generation: Both these intelligent technologies help create natural interactions with analytics platforms, but they significantly differ from each other. NLP involves asking the right questions, while NLG looks for and generates suitable answers. The current market is more receptive towards NLP, especially with the increasing usage of language interfaces such as Siri, Cortana, Alexa etc. NLP has already hit the market with businesses incorporating it in their communications through analytics platforms. However, experimentation is still ongoing to find the best level use cases that will truly benefit businesses. NLG has been around for several years, and has recently been used to augment visual data representations for businesses. For example, sporting events are being represented through text by using NLG. The combination of NLG and business analytics is a relatively new field, and is expected to catch on fast.
3. Embedded Analytics (EA): The objective of analytics is to provide useful business insights that help make business decisions accurate and easier. The best bet to do this is by embedding analytics into business applications. This will not only enable access to every business stakeholder, but make analytics a way of life. A number of modern analytics platform vendors have made this possible not only for enterprises, but also for their customers, partners and other stakeholders . Enterprise leaders must realise the huge opportunity that embedded analytics presents for business decision-making. Some organizations are early innovators, playing around with this approach, while others are waiting for the trend to go mainstream.
These are three major trends that are set to change the market realities in the enterprise analytics software space. The degree of adoption of each depends on how the end user benefits with each of these in the overall business landscape.